Advances on classical combinatorial problems have shown promising results in downstream applications to novel or under-studied optimization problems in the physical sciences [1]}, [2]}, [3]} and computer architecture [4]}, [5]}, [6]}, where the development of exact solvers is expensive and intractable.
For example, autoregressive architectures provide a strong inductive bias for device placement optimization problems [7]}, [8]}, while non-autoregressive models [9]} are competitive with autoregressive approaches [10]}, [11]} for molecule generation tasks.
